Laplacian-based Semi-Supervised Learning in Multilayer Hypergraphs by Coordinate Descent

TL;DR

This paper introduces a coordinate descent approach for semi-supervised learning on multilayer hypergraphs, extending traditional graph methods and demonstrating its effectiveness through experiments.

Contribution

It extends semi-supervised learning to multilayer hypergraphs and compares coordinate descent methods with gradient descent for optimization.

Findings

Coordinate descent methods outperform gradient descent in experiments.

The approach effectively handles multilayer hypergraph data.

Experimental results on synthetic and real datasets validate the method.

Abstract

Graph Semi-Supervised learning is an important data analysis tool, where given a graph and a set of labeled nodes, the aim is to infer the labels to the remaining unlabeled nodes. In this paper, we start by considering an optimization-based formulation of the problem for an undirected graph, and then we extend this formulation to multilayer hypergraphs. We solve the problem using different coordinate descent approaches and compare the results with the ones obtained by the classic gradient descent method. Experiments on synthetic and real-world datasets show the potential of using coordinate descent methods with suitable selection rules.